An amount of information available by way of the World Wide Web has grown exponentially, such that billions of items are available by way of the World Wide Web. This explosive growth of information available on the web has not only created a crucial challenge for search engine companies in connection with handling large scale data, but has also increased the difficulty for a user to manage his/her information needs. For instance, it may be difficult for a user to compose a succinct and precise query to represent his/her information needs.
Instead of pushing the burden of generating succinct search queries to the user, search engines have been configured to provide increasingly relevant search results. More particularly, a search engine can be configured to retrieve documents relevant to a user query by comparing attributes of documents together with other features such as anchor text, and can return documents that best match the query. Conventional search engines can also consider previous user searches, user location, and current events, amongst other information in connection with providing the most relevant search results to a query issued by a user. The user is typically shown a ranked list of universal resource locators (URLs) in response to providing a query to the search engine.
Moreover, at least some search engines are configured with functionality to provide a user with alternative queries to a query provided by the user. Such alternative queries can be configured to correct possible spelling mistakes, may be configured to provide the user with information that is related but non-identical to information retrieved by way of the query provided by the user, etc. These query suggestions typically include queries issued by users subsequent to the users issuing an initial query. For instance, if a user types a query “msg” to a search engine, the user may be provided with quite a few alternative potential queries such as “Madison Square Garden,” “Monosodium Glutamate,” and others.